An Algorithm for Finding Intrinsic Dimensionality of Data
- 1 February 1971
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Computers
- Vol. C-20 (2) , 176-183
- https://doi.org/10.1109/t-c.1971.223208
Abstract
An algorithm for the analysis of multivariant data is presented along with some experimental results. The basic idea of the method is to examine the data in many small subregions, and from this determine the number of governing parameters, or intrinsic dimensionality. This intrinsic dimensionality is usually much lower than the dimensionality that is given by the standard Karhunen-Loève technique. An analysis that demonstrates the feasability of this approach is presented.Keywords
This publication has 9 references indexed in Scilit:
- Representation of random processes using the finite Karhunen-Loève expansionInformation and Control, 1970
- Randomly Generated Nonlinear Transformations for Pattern RecognitionIEEE Transactions on Systems Science and Cybernetics, 1969
- The intrinsic dimensionality of signal collectionsIEEE Transactions on Information Theory, 1969
- A Nonlinear Mapping for Data Structure AnalysisIEEE Transactions on Computers, 1969
- Statistical estimation of the intrinsic dimensionality of data collectionsInformation and Control, 1968
- Nonmetric Multidimensional Scaling: A Numerical MethodPsychometrika, 1964
- Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesisPsychometrika, 1964
- The Analysis of Proximities: Multidimensional Scaling with an Unknown Distance Function. IIPsychometrika, 1962
- The Analysis of Proximities: Multidimensional Scaling with an Unknown Distance Function. I.Psychometrika, 1962